Search Results
Integer Quantization for Deep Learning Inference: Principles and Empirical Evaluation
Quantization techniques can reduce the size of Deep Neural Networks and improve inference latency and throughput by taking advantage of high throughput integer instructions.
Deep Learning with Differential Privacy
Machine learning techniques based on neural networks are achieving remarkable results in a wide variety of domains.
ShakeDrop Regularization for Deep Residual Learning
In this paper, to relieve the overfitting effect of ResNet and its improvements (i. e., Wide ResNet, PyramidNet, and ResNeXt), we propose a new regularization method called ShakeDrop regularization.
Deep Residual Learning for Image Recognition
Deep residual nets are foundations of our submissions to ILSVRC & COCO 2015 competitions, where we also won the 1st places on the tasks of ImageNet detection, ImageNet localization, COCO detection, and COCO segmentation.
Ranked #1 on
Image Classification
on cifar100
Xception: Deep Learning with Depthwise Separable Convolutions
We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution).
Ranked #5 on
Classification
on InDL
Deep Learning Recommendation Model for Personalization and Recommendation Systems
With the advent of deep learning, neural network-based recommendation models have emerged as an important tool for tackling personalization and recommendation tasks.
Deep Image Retrieval: Learning global representations for image search
We propose a novel approach for instance-level image retrieval.
Ranked #3 on
Image Retrieval
on Oxf105k
Deep Reinforcement Learning with Double Q-learning
The popular Q-learning algorithm is known to overestimate action values under certain conditions.
Ranked #10 on
Atari Games
on Atari 2600 Gopher
Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks
In recent years, supervised learning with convolutional networks (CNNs) has seen huge adoption in computer vision applications.
Ranked #9 on
Image Clustering
on Tiny-ImageNet
Playing Atari with Deep Reinforcement Learning
We present the first deep learning model to successfully learn control policies directly from high-dimensional sensory input using reinforcement learning.
Ranked #1 on
Atari Games
on Atari 2600 Pong
